{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","collapsed_sections":["gaD7ugivEL2R"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["# 数据集导入(预制链接)"],"metadata":{"id":"kBRw5QHhBkax"}},{"cell_type":"markdown","source":["## 官方版本数据导入"],"metadata":{"id":"gaD7ugivEL2R"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hfHjWRgKBR47","outputId":"8915f605-e3f1-4d47-d9bb-fa9cffe63158","collapsed":true},"outputs":[{"output_type":"stream","name":"stdout","text":["--2024-07-10 12:14:53--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n","Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.251.31.132, 2a00:1450:4013:c1a::84\n","Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.251.31.132|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1084182095 (1.0G) [application/octet-stream]\n","Saving to: ‘Dataset.zip’\n","\n","Dataset.zip         100%[===================>]   1.01G  59.6MB/s    in 14s     \n","\n","2024-07-10 12:15:07 (74.5 MB/s) - ‘Dataset.zip’ saved [1084182095/1084182095]\n","\n","Archive:  Dataset.zip\n","   creating: Dataset/\n","   creating: Dataset/Testset_track_A/\n","   creating: Dataset/Testset_track_A/Inference/\n","  inflating: Dataset/Testset_track_A/Inference/mesh_658.ply  \n","  inflating: Dataset/Testset_track_A/Inference/mesh_659.ply  \n","  inflating: Dataset/Testset_track_A/Inference/mesh_660.ply  \n","  inflating: Dataset/Testset_track_A/Inference/mesh_662.ply  \n","  inflating: Dataset/Testset_track_A/Inference/mesh_663.ply  \n","  inflating: Dataset/Testset_track_A/Inference/mesh_664.ply  \n","  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Dataset/Training_data/Label/press_549.npy  \n","  inflating: Dataset/Training_data/Label/press_550.npy  \n","  inflating: Dataset/Training_data/Label/press_551.npy  \n","  inflating: Dataset/Training_data/Label/press_552.npy  \n","  inflating: Dataset/Training_data/Label/press_553.npy  \n","  inflating: Dataset/Training_data/Label/press_554.npy  \n","  inflating: Dataset/Training_data/Label/press_555.npy  \n","  inflating: Dataset/Training_data/Label/press_560.npy  \n","  inflating: Dataset/Training_data/Label/press_561.npy  \n","  inflating: Dataset/Training_data/Label/press_562.npy  \n","  inflating: Dataset/Training_data/Label/press_564.npy  \n","  inflating: Dataset/Training_data/Label/press_565.npy  \n","  inflating: Dataset/Training_data/Label/press_566.npy  \n","  inflating: Dataset/Training_data/Label/press_567.npy  \n","  inflating: Dataset/Training_data/Label/press_568.npy  \n","  inflating: Dataset/Training_data/Label/press_569.npy  \n","  inflating: Dataset/Training_data/Label/press_572.npy  \n","  inflating: Dataset/Training_data/Label/press_573.npy  \n","  inflating: Dataset/Training_data/Label/press_574.npy  \n","  inflating: Dataset/Training_data/Label/press_576.npy  \n","  inflating: Dataset/Training_data/Label/press_577.npy  \n","  inflating: Dataset/Training_data/Label/press_579.npy  \n","  inflating: Dataset/Training_data/Label/press_581.npy  \n","  inflating: Dataset/Training_data/Label/press_582.npy  \n","  inflating: Dataset/Training_data/Label/press_583.npy  \n","  inflating: Dataset/Training_data/Label/press_584.npy  \n","  inflating: Dataset/Training_data/Label/press_587.npy  \n","  inflating: Dataset/Training_data/Label/press_588.npy  \n","  inflating: Dataset/Training_data/Label/press_589.npy  \n","  inflating: Dataset/Training_data/Label/press_591.npy  \n","  inflating: Dataset/Training_data/Label/press_593.npy  \n","  inflating: Dataset/Training_data/Label/press_594.npy  \n","  inflating: Dataset/Training_data/Label/press_595.npy  \n","  inflating: Dataset/Training_data/Label/press_596.npy  \n","  inflating: Dataset/Training_data/Label/press_597.npy  \n","  inflating: Dataset/Training_data/Label/press_598.npy  \n","  inflating: Dataset/Training_data/Label/press_600.npy  \n","  inflating: Dataset/Training_data/Label/press_602.npy  \n","  inflating: Dataset/Training_data/Label/press_604.npy  \n","  inflating: Dataset/Training_data/Label/press_608.npy  \n","  inflating: Dataset/Training_data/Label/press_610.npy  \n","  inflating: Dataset/Training_data/Label/press_611.npy  \n","  inflating: Dataset/Training_data/Label/press_612.npy  \n","  inflating: Dataset/Training_data/Label/press_613.npy  \n","  inflating: Dataset/Training_data/Label/press_615.npy  \n","  inflating: Dataset/Training_data/Label/press_616.npy  \n","  inflating: Dataset/Training_data/Label/press_617.npy  \n","  inflating: Dataset/Training_data/Label/press_618.npy  \n","  inflating: Dataset/Training_data/Label/press_620.npy  \n","  inflating: Dataset/Training_data/Label/press_621.npy  \n","  inflating: Dataset/Training_data/Label/press_622.npy  \n","  inflating: Dataset/Training_data/Label/press_623.npy  \n","  inflating: Dataset/Training_data/Label/press_625.npy  \n","  inflating: Dataset/Training_data/Label/press_626.npy  \n","  inflating: Dataset/Training_data/Label/press_627.npy  \n","  inflating: Dataset/Training_data/Label/press_628.npy  \n","  inflating: Dataset/Training_data/Label/press_629.npy  \n","  inflating: Dataset/Training_data/Label/press_630.npy  \n","  inflating: Dataset/Training_data/Label/press_631.npy  \n","  inflating: Dataset/Training_data/Label/press_632.npy  \n","  inflating: Dataset/Training_data/Label/press_633.npy  \n","  inflating: Dataset/Training_data/Label/press_634.npy  \n","  inflating: Dataset/Training_data/Label/press_635.npy  \n","  inflating: Dataset/Training_data/Label/press_636.npy  \n","  inflating: 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Dataset/Training_data/Label/press_656.npy  \n","  inflating: Dataset/Training_data/Label/press_657.npy  \n","  inflating: Dataset/Training_data/train_pressure_min_std.txt  \n","  inflating: Dataset/Training_data/watertight_global_bounds.txt  \n","  inflating: Dataset/Training_data/watertight_meshes.txt  \n"]}],"source":["# !wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n","# !unzip Dataset.zip"]},{"cell_type":"markdown","source":["## 百度Baseline版本数据导入"],"metadata":{"id":"z0Sek0wtEs5n"}},{"cell_type":"code","source":["# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/1638f9c292b9437bb46885186407a63e584856c91f9f4c18908b87abd46471e0?responseContentDisposition=attachment%3B%20filename%3Dtrack_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A02%3A25Z%2F-1%2F%2Fcfdfd6b6a9e096c761ee8e7d863d586741c69a9e6de89f9c3696706d35f8b265\" -c -O 'track_B.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/2dddd05e577849ad95e1fe1133d3af29d13085ac0cfd499c853ff5d9df2ac07f?responseContentDisposition=attachment%3B%20filename%3Dtrain_data.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A23%3A33Z%2F-1%2F%2F8540633c7e39fddf8471d6d8206c3b761748c58c06005acb218593a8df19d7f1\" -c -O 'train_data.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/a96dc8ba8201445b966980a0a48f52705338a48e29e64c53bddb7ef8861c5123?responseContentDisposition=attachment%3B%20filename%3Dtrack_A.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-06T07%3A54%3A51Z%2F-1%2F%2F17b5155bd16a8af1e4af971498082687656af7fcecfc5a8e57591b85053210ec\" -c -O 'track_A.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/dcaba9f0e87549e395e1682a4a0a43c547a896034e3f4417a4d10ba85a949944?responseContentDisposition=attachment%3B%20filename%3DPaddleScience.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-11T16%3A01%3A57Z%2F-1%2F%2Fb2149791689d3b19c02a86782229901cb75743d07c7163dfa185fea286cd1f02\" -c -O 'PaddleScience.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\" -c -O 'train_track_B.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/7877c2fd300345599ed3365feda50425f4857caa71bf4af9bf047fb08e35aa97?responseContentDisposition=attachment%3B%20filename%3Dmesh_B_0603.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T11%3A48%3A26Z%2F-1%2F%2Fec6cce492ba6afff841ef197860065742a7ba8220def02c1cbc311333aa993b2\" -c -O 'mesh_B_0603.zip'\n","# !wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/a02dba5700974c6a811f579fff216ccf9a4129b849994dcca99390b222c28572?responseContentDisposition=attachment%3B%20filename%3D3rd_lib.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-08T05%3A29%3A20Z%2F-1%2F%2Fef71819149664a3c8438d3dfd02544e77cdb4fda679efbdd402b4e2c77a2c06d\" -c -O '3rd_lib.zip'\n"],"metadata":{"id":"GTV_YDaxEsd3","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# !unzip 3rd_lib.zip\n","# !unzip PaddleScience.zip"],"metadata":{"id":"cs8g_rlLjg-W","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# !mkdir -p track_B && unzip -o track_B.zip -d track_B/\n","# !mkdir -p train_data && unzip -o train_data.zip -d train_data/\n","# !mkdir -p track_A && unzip -o track_A.zip -d track_A/\n","# !mkdir -p train_track_B && unzip -o train_track_B.zip -d train_track_B/\n","# !mkdir -p mesh_B_0603 && unzip -o mesh_B_0603.zip -d mesh_B_0603/"],"metadata":{"id":"OS4r3PcokLdA","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 自定义导入(在下面代码块导入并解压您的数据集)"],"metadata":{"id":"kY81z-fCgPfK"}},{"cell_type":"code","source":["!pip install gdown"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2aR4aYAc2IN7","executionInfo":{"status":"ok","timestamp":1720937470112,"user_tz":-480,"elapsed":26272,"user":{"displayName":"huiyu yang","userId":"01765890537685750077"}},"outputId":"5d5a6f71-fd8b-4d4a-eba2-9b731c906de3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n","Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.15.4)\n","Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.4)\n","Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.7.4)\n","Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"]}]},{"cell_type":"code","source":["import gdown\n","import os\n","import zipfile\n","\n","def unzip_file(zip_path, extract_to):\n","  # 检查目标目录是否存在，如果不存在则创建\n","  if not os.path.exists(extract_to):\n","    os.makedirs(extract_to)\n","\n","  # 打开ZIP文件\n","  with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    # 解压缩所有文件\n","    zip_ref.extractall(extract_to)\n","\n","###################### code ######################\n","file_id = '1DXwS6ApRTLbdPapbjQNu-r7omKvThIT8' # 文件ID\n","destination = '/code.zip' # 文件下载路径\n","download_url = f'https://drive.google.com/uc?id={file_id}' # 构建下载URL\n","if not os.path.exists(destination):\n","  gdown.download(download_url, destination, quiet=False) # 下载文件\n","\n","zip_file_path = destination # 压缩文件路径\n","extract_directory = '/content/code' # 解压目录\n","unzip_file(zip_file_path, extract_directory)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gaNdGSME18jb","executionInfo":{"status":"ok","timestamp":1720937474943,"user_tz":-480,"elapsed":4835,"user":{"displayName":"huiyu yang","userId":"01765890537685750077"}},"outputId":"2ebdff7c-86bf-4c66-9e68-2b9c20317d96"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading...\n","From: https://drive.google.com/uc?id=1DXwS6ApRTLbdPapbjQNu-r7omKvThIT8\n","To: /code.zip\n","100%|██████████| 129k/129k [00:00<00:00, 16.3MB/s]\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"ITzT8s2wgZG0"}},{"cell_type":"markdown","source":["# 包导入规范"],"metadata":{"id":"PmlOGK6yPVGu"}},{"cell_type":"code","source":["!pip install meshio\n","!pip install open3d\n","!pip install einops\n","!pip install paddlepaddle\n","!pip install timm\n","!pip install spconv\n","!pip install requests"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Zsx-qsS42Epq","executionInfo":{"status":"ok","timestamp":1720937649652,"user_tz":-480,"elapsed":174712,"user":{"displayName":"huiyu yang","userId":"01765890537685750077"}},"outputId":"8501ef72-54d8-4035-b1fc-fe44803b8ae2","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting meshio\n","  Downloading meshio-5.3.5-py3-none-any.whl (166 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/166.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.2/166.2 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from meshio) (1.25.2)\n","Requirement already satisfied: rich in 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in /usr/local/lib/python3.10/dist-packages (from open3d) (9.4.0)\n","Requirement already satisfied: matplotlib>=3 in /usr/local/lib/python3.10/dist-packages (from open3d) (3.7.1)\n","Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.10/dist-packages (from open3d) (2.0.3)\n","Requirement already satisfied: pyyaml>=5.4.1 in /usr/local/lib/python3.10/dist-packages (from open3d) (6.0.1)\n","Requirement already satisfied: scikit-learn>=0.21 in /usr/local/lib/python3.10/dist-packages (from open3d) (1.2.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from open3d) (4.66.4)\n","Collecting pyquaternion (from open3d)\n","  Downloading pyquaternion-0.9.9-py3-none-any.whl (14 kB)\n","Requirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from dash>=2.6.0->open3d) (2.2.5)\n","Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from dash>=2.6.0->open3d) 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sha256=f8fa322aee6d1ca6c2b9c0e87ee4dbea2c66b8da55d6c223e55e33fe4b68cff6\n","  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n","Successfully built fire\n","Installing collected packages: ninja, pybind11, portalocker, lark, fire, ccimport, pccm, cumm, spconv\n","Successfully installed ccimport-0.4.2 cumm-0.4.11 fire-0.6.0 lark-1.1.9 ninja-1.11.1.1 pccm-0.4.11 portalocker-2.10.1 pybind11-2.13.1 spconv-2.3.6\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.7.4)\n"]}]},{"cell_type":"code","source":["import sys\n","sys.path.append(\"code/code/model\")\n","\n","import re\n","import meshio\n","import os\n","import yaml\n","import shutil\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import numpy as np\n","import pandas as pd\n","import open3d as o3d\n","import matplotlib.pyplot as plt\n","\n","from matplotlib import rcParams\n","from timeit import default_timer\n","from einops import rearrange\n","from src.utils.utils import dict2namespace, LpLoss, count_params\n","from src.data.cfd_datamodule import CFDDataModule\n","from src.networks.Transolver import Model\n","from src.networks.PTV3 import Point\n","from torch.utils.data import TensorDataset, DataLoader\n","from torch.nn.init import xavier_uniform_\n","\n","def set_random_seed(seed):\n","    np.random.seed(seed)\n","    torch.manual_seed(seed)\n","    torch.cuda.manual_seed(seed)\n","    torch.cuda.manual_seed_all(seed)\n","\n","set_random_seed(1234)"],"metadata":{"id":"2I4EqBzU2JcH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 直接导入(建议)"],"metadata":{"id":"_Mh7pXUyYOvl"}},{"cell_type":"code","source":["# !pip install timm paddlepaddle\n","# import timm\n","# import paddle"],"metadata":{"id":"XLNZMxywPYe1","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 通过requirements.txt一次性导入"],"metadata":{"id":"sRLfyacGYGcY"}},{"cell_type":"code","source":["# ###或者通过requirement导入，requirements需要在B.zip压缩包默认路径下\n","# !pip install -r requirements.txt"],"metadata":{"id":"08ZsNhl5YFkj"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 额外数据导入\n","（此处导入权重文件和额外数据集，在此之外的导入将有被判违规的风险，这里以导入随机生成的Track C的A榜样例提交的zip为例子）"],"metadata":{"id":"DUxmPjWWV1sr"}},{"cell_type":"markdown","source":["数据下载链接：https://drive.google.com/file/d/1Vb740MGw7dMN943bRTNvy_f_qFMB9sjX/view?usp=sharing\n","\n","论文：Li, Zongyi, et al. \"Geometry-informed neural operator for large-scale 3d pdes.\" Advances in Neural Information Processing Systems 36 (2024).\n","\n","说明：原始论文数据含798个样本，比赛官方抽取了其中500个作为训练集，50个作为测试集，我们排除了这550个数据，使用剩下的248个样本作为额外数据，未踩到测试集"],"metadata":{"id":"Le_pZN-im3qb"}},{"cell_type":"code","source":["# !wget --header=\"Host: openatom-competition.oss-cn-beijing.aliyuncs.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://competition.atomgit.com/\" \"https://openatom-competition.oss-cn-beijing.aliyuncs.com/files/House%20price%20predict/Example_C.zip\" -c -O 'Example_C.zip'\n","# !unzip Example_C.zip"],"metadata":{"id":"H8YjMlYcYmei","collapsed":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["###################### data ######################\n","file_id = '13kPNzk4oGrNTEEFR3I_mbcwgcjGHi1-Y' # 文件ID\n","destination = '/Dataset.zip' # 文件下载路径\n","download_url = f'https://drive.google.com/uc?id={file_id}' # 构建下载URL\n","if not os.path.exists(destination):\n","  gdown.download(download_url, destination, quiet=False) # 下载文件\n","\n","zip_file_path = destination # 压缩文件路径\n","extract_directory = '/content/Dataset' # 解压目录\n","unzip_file(zip_file_path, extract_directory)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_C2tHpsH2dFn","executionInfo":{"status":"ok","timestamp":1720937677614,"user_tz":-480,"elapsed":13051,"user":{"displayName":"huiyu yang","userId":"01765890537685750077"}},"outputId":"ca4a2887-9e03-4629-b750-28e64145a4f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading...\n","From (original): https://drive.google.com/uc?id=13kPNzk4oGrNTEEFR3I_mbcwgcjGHi1-Y\n","From (redirected): https://drive.google.com/uc?id=13kPNzk4oGrNTEEFR3I_mbcwgcjGHi1-Y&confirm=t&uuid=ba300198-027b-490c-804a-77df0a45a2e3\n","To: /Dataset.zip\n","100%|██████████| 192M/192M [00:02<00:00, 70.4MB/s]\n"]}]},{"cell_type":"markdown","source":["# 主要库版本检查以及随机种子锁定"],"metadata":{"id":"p7YDBByYeYsB"}},{"cell_type":"markdown","source":["## 版本检查"],"metadata":{"id":"NHfBLsBMeeWB"}},{"cell_type":"code","source":["# import paddle\n","# import torch\n","# import tensorflow as tf\n","# import numpy as np\n","\n","# print(\"PaddlePaddle Version:\", paddle.__version__)\n","# print(\"PyTorch Version:\", torch.__version__)\n","# print(\"TensorFlow Version:\", tf.__version__)\n","# print(\"NumPy Version:\", np.__version__)"],"metadata":{"id":"qHPSLhvjggLC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 随机种子锁定"],"metadata":{"id":"90Bu7fXRftyr"}},{"cell_type":"code","source":["# import numpy as np\n","# import torch\n","# # import tensorflow as tf\n","# import random\n","# import os\n","\n","# def seed_everything(seed=42):\n","#   random.seed(seed)           # Python 内置的随机库\n","#   np.random.seed(seed)        # NumPy 随机数种子\n","#   os.environ['PYTHONHASHSEED'] = str(seed)  # 环境变量\n","\n","#   # PyTorch 设置\n","#   torch.manual_seed(seed)\n","#   torch.cuda.manual_seed(seed)\n","#   torch.cuda.manual_seed_all(seed)  # 如果使用多 GPU\n","#   torch.backends.cudnn.deterministic = True\n","#   torch.backends.cudnn.benchmark = False\n","\n","# # 调用函数以设置种子\n","# seed_everything(1234)\n"],"metadata":{"id":"Y_6xjjJIef1f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# -------------------------- parameters --------------------------\n","# read the parameters file\n","with open(f'./code/code/configs/Transolver.yml', 'r') as f:\n","    config = yaml.safe_load(f)\n","config = dict2namespace(config)\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","config.model.n_layer=5\n","config.training.epochs=350\n","config.training.lr=0.0005\n","config.training.scheduler_step=50\n","config.training.scheduler_gamma=0.8\n","\n","# -------------------------- data_generation --------------------------\n","training_datamodule = CFDDataModule(data_dir='Dataset/Dataset/Training_data', n_train=500, n_test=0, normal=config.normalization)\n","\n","data_dir = \"Dataset/Dataset/full_data/\"\n","config.training_data_dir = data_dir\n","filename = \"watertight_meshes_748.txt\"\n","with open(data_dir + filename, \"r\") as fp:\n","  mesh_ind = fp.read().split(\"\\n\")\n","  mesh_ind = [a for a in mesh_ind]\n","\n","mesh_lst = []\n","for ind in mesh_ind:\n","  mesh_path = data_dir + \"Feature/\" + f\"mesh_{ind}.ply\"\n","  mesh = o3d.io.read_point_cloud(str(mesh_path))\n","  mesh = np.asarray(mesh.points)\n","  if mesh.shape[0] == 3682:\n","    mesh = np.concatenate((mesh[0:16], mesh[112:]), axis=0)\n","  mesh_lst.append(mesh[None, ...])\n","mesh_lst = np.concatenate(mesh_lst, axis=0)\n","\n","press_lst = []\n","for ind in mesh_ind:\n","  press_path = data_dir + \"Label/\" + f\"press_{ind}.npy\"\n","  press = np.load(press_path)\n","  press = np.concatenate((press[0:16], press[112:]), axis=0)\n","  press_lst.append(press[None, ...])\n","press_lst = np.concatenate(press_lst, axis=0)\n","\n","train_x = torch.from_numpy(mesh_lst).float()\n","train_y = torch.from_numpy(press_lst).float()\n","\n","\n","# -------------------------- serialization_generation --------------------------\n","######################## training_data ########################\n","serialized_files = os.path.join('Dataset/Dataset/full_data', 'Serialization')\n","if os.path.exists(serialized_files) == False:\n","  order_list = []\n","  inverse_list = []\n","\n","  for i in range(train_x.shape[0]):\n","    data_dict = dict(coord=train_x[[i], ...], grid_size = torch.tensor([0.05,0.05,0.05]))\n","    B,N,C = data_dict['coord'].shape\n","    data_dict['batch'] = torch.arange(B).repeat_interleave(N).cuda()\n","    data_dict['coord'] = data_dict['coord'].view(B*N,C).cuda()\n","    point = Point(data_dict)\n","    point.serialization(order=[\"z\", \"z-trans\", \"hilbert\", \"hilbert-trans\"], shuffle_orders=False)\n","    order_list.append(point['serialized_order'][None, ...].cpu())\n","    inverse_list.append(point['serialized_inverse'][None, ...].cpu())\n","\n","  train_serialized_order = torch.cat(order_list, dim=0) # (748,4,3586)\n","  train_serialized_inverse = torch.cat(inverse_list, dim=0) # (748,4,3586)\n","\n","  os.makedirs(serialized_files)\n","\n","  for i, index in enumerate(mesh_ind):\n","    order = train_serialized_order[i].numpy()\n","    inverse = train_serialized_inverse[i].numpy()\n","    np.save(os.path.join(serialized_files, f'order_{index}.npy'), order)\n","    np.save(os.path.join(serialized_files, f'inverse_{index}.npy'), inverse)\n","\n","else:\n","  order_list = []\n","  inverse_list = []\n","  for i, index in enumerate(mesh_ind):\n","    order = np.load(os.path.join(serialized_files, f'order_{index}.npy'))\n","    inverse = np.load(os.path.join(serialized_files, f'inverse_{index}.npy'))\n","    order_list.append(order[None, ...])\n","    inverse_list.append(inverse[None, ...])\n","\n","  train_serialized_order = torch.from_numpy(np.concatenate(order_list, axis=0)) # (748,4,3586)\n","  train_serialized_inverse = torch.from_numpy(np.concatenate(inverse_list, axis=0)) # (748,4,3586)\n","\n","######################## testing_data ########################\n","serialized_files = os.path.join('Dataset/Dataset/Testing_data', 'Serialization')\n","if os.path.exists(serialized_files) == False:\n","  order_list = []\n","  inverse_list = []\n","\n","  for i in range(test_x.shape[0]):\n","    data_dict = dict(coord=train_x[[i], ...], grid_size = torch.tensor([0.05,0.05,0.05]))\n","    B,N,C = data_dict['coord'].shape\n","    data_dict['batch'] = torch.arange(B).repeat_interleave(N).cuda()\n","    data_dict['coord'] = data_dict['coord'].view(B*N,C).cuda()\n","    point = Point(data_dict)\n","    point.serialization(order=[\"z\", \"z-trans\", \"hilbert\", \"hilbert-trans\"], shuffle_orders=False)\n","    order_list.append(point['serialized_order'][None, ...].cpu())\n","    inverse_list.append(point['serialized_inverse'][None, ...].cpu())\n","\n","  test_serialized_order = torch.cat(order_list, dim=0) # (50,4,3586)\n","  test_serialized_inverse = torch.cat(inverse_list, dim=0) # (50,4,3586)\n","\n","  os.makedirs(serialized_files)\n","\n","  filename = \"/watertight_meshes.txt\"\n","  with open('Dataset/Dataset/Testing_data/' + filename, \"r\") as fp:\n","    mesh_ind = fp.read().split(\"\\n\")\n","    mesh_ind = [a for a in mesh_ind]\n","\n","  for i, index in enumerate(mesh_ind):\n","    order = test_serialized_order[i].numpy()\n","    inverse = test_serialized_inverse[i].numpy()\n","    np.save(os.path.join(serialized_files, f'order_{index}.npy'), order)\n","    np.save(os.path.join(serialized_files, f'inverse_{index}.npy'), inverse)\n","\n","else:\n","  filename = \"/watertight_meshes.txt\"\n","  with open('Dataset/Dataset/Testing_data/' + filename, \"r\") as fp:\n","    mesh_ind = fp.read().split(\"\\n\")\n","    mesh_ind = [a for a in mesh_ind]\n","\n","  order_list = []\n","  inverse_list = []\n","  for i, index in enumerate(mesh_ind):\n","    order = np.load(os.path.join(serialized_files, f'order_{index}.npy'))\n","    inverse = np.load(os.path.join(serialized_files, f'inverse_{index}.npy'))\n","    order_list.append(order[None, ...])\n","    inverse_list.append(inverse[None, ...])\n","\n","  test_serialized_order = torch.from_numpy(np.concatenate(order_list, axis=0)) # (50,4,3586)\n","  test_serialized_inverse = torch.from_numpy(np.concatenate(inverse_list, axis=0)) # (50,4,3586)\n","\n","\n","# -------------------------- data_loader --------------------------\n","train_dataset = TensorDataset(train_x, train_y, train_serialized_order, train_serialized_inverse)\n","train_loader = DataLoader(train_dataset, batch_size=config.training.batch_size, shuffle=True)\n","\n","\n","# -------------------------- define --------------------------\n","model = Model(n_hidden=config.model.hidden_dim,\n","        n_layers=config.model.depth,\n","        space_dim=config.model.input_dim,\n","        fun_dim=0,\n","        n_head=config.model.num_heads,\n","        mlp_ratio=config.model.mlp_ratio,\n","        out_dim=config.model.output_dim,\n","        slice_num=config.model.num_slices,\n","        patch_size=config.model.patch_size,\n","        shift=config.model.shift,\n","        n_iter=config.model.n_layer,\n","        unified_pos=0).to(device)\n","\n","info = f'count_params: {count_params(model)}'\n","print(info)\n","\n","optimizer = torch.optim.Adam(model.parameters(), lr=config.training.lr)\n","scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=config.training.scheduler_step, gamma=config.training.scheduler_gamma)\n","\n","training_datamodule.output_normalization.to(device)\n","\n","mse_train = []\n","mse_test = []\n","min_test_loss = 1\n","\n","L2_fn = LpLoss(reduction=False)\n","loss_fn = nn.MSELoss(reduction='none')\n","\n","\n","# -------------------------- training --------------------------\n","t0 = default_timer()\n","for ep in range(config.training.epochs):\n","  model.train()\n","  t1 = default_timer()\n","  for n_iter, (x, y, order, inverse) in enumerate(train_loader):\n","    x = x.to(device)\n","    y = y.to(device)\n","    order = order.to(device)\n","    inverse = inverse.to(device)\n","\n","    y_hat = model(x, order, inverse)\n","\n","    if config.normalization == True:\n","      y_hat = training_datamodule.output_normalization.decode(y_hat)\n","\n","    train_loss = torch.mean(loss_fn(y_hat[:,:,0], y))\n","\n","    optimizer.zero_grad()\n","    train_loss.backward()\n","    optimizer.step()\n","\n","  mse_train.append(train_loss.item())\n","  scheduler.step()\n","\n","  t2 = default_timer()\n","\n","  info = f'{ep} {(t2-t1):.2f} train_loss:{train_loss.item():.4f}'\n","  print(info)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VjIXs9bg2s6i","outputId":"3b26f05d-61c4-46e8-f6d5-443de7920e7e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["count_params: 8127809\n"]}]},{"cell_type":"markdown","source":["# 以C榜为例的输出规范\n","(将答案文件夹压缩为B_result.zip,用于被程序识别，假设文件路径是Example_C)"],"metadata":{"id":"MBCalHN7bBji"}},{"cell_type":"code","source":["# -------------------------- data_generation --------------------------\n","def get_all_filenames(directory):\n","  filenames = []\n","  files = []\n","  for filename in os.listdir(directory):\n","    filenames.append(directory + '/' + filename)\n","    files.append(filename)\n","  return filenames, files\n","\n","filenames, files = get_all_filenames('Dataset/Dataset/Testing_data/Feature')\n","\n","mesh_lst = []\n","order_lst = []\n","inverse_lst = []\n","\n","for filename in filenames:\n","  mesh = o3d.io.read_point_cloud(filename)\n","  mesh = np.asarray(mesh.points)\n","  mesh_lst.append(mesh[None, ...])\n","\n","  order = np.load(f'Dataset/Dataset/Testing_data/Serialization/order_{filename[-7:-4]}.npy')\n","  inverse = np.load(f'Dataset/Dataset/Testing_data/Serialization/inverse_{filename[-7:-4]}.npy')\n","  order_lst.append(order[None, ...])\n","  inverse_lst.append(inverse[None, ...])\n","\n","mesh_lst = np.concatenate(mesh_lst, axis=0)\n","train_x = torch.from_numpy(mesh_lst).float() # (50,3586,3)\n","\n","order_lst = np.concatenate(order_lst, axis=0)\n","train_serialized_order = torch.from_numpy(order_lst) # (50,4,3586)\n","\n","inverse_lst = np.concatenate(inverse_lst, axis=0)\n","train_serialized_inverse = torch.from_numpy(inverse_lst) # (50,4,3586)\n","\n","train_dataset = TensorDataset(train_x, train_serialized_order, train_serialized_inverse)\n","train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False)\n","\n","\n","# -------------------------- evaluation --------------------------\n","y_hat_list = []\n","model.eval()\n","with torch.no_grad():\n","  for n_iter, (x, order, inverse) in enumerate(train_loader):\n","    x = x.to(device)\n","    order = order.to(device)\n","    inverse = inverse.to(device)\n","\n","    y_hat = model(x, order, inverse)\n","    y_hat = y_hat[..., 0] * 48.095516 - 37.089962\n","    y_hat_list.append(y_hat.cpu().numpy())\n","\n","y_hat_list = np.concatenate(y_hat_list, axis=0)\n","\n","npy_dir = 'B_Results'\n","if not os.path.exists(npy_dir):\n","  os.makedirs(npy_dir)\n","\n","for i, file in enumerate(files):\n","  index = file[5:8]\n","  np.save(npy_dir + '/' + f'press_{index}.npy', y_hat_list[i])\n","\n","\n","import zipfile\n","def zip_dir(path, output=None):\n","  \"\"\"压缩指定目录\"\"\"\n","  output = output or os.path.basename(path) + '.zip' # 压缩文件的名字\n","  zip = zipfile.ZipFile(output, 'w', zipfile.ZIP_DEFLATED)\n","  for root, dirs, files in os.walk(path):\n","    relative_root = '' if root == path else root.replace(path, '') + os.sep  # 计算文件相对路径\n","    for filename in files:\n","      zip.write(os.path.join(root, filename), relative_root + filename)  # 文件路径 压缩文件路径（相对路径）\n","  zip.close()\n","\n","output = 'B_Results.zip'\n","zip_dir(npy_dir, output)"],"metadata":{"id":"erLbyNFK26o8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# !zip -r B_result.zip ./Example_C/"],"metadata":{"id":"_R4ymI9BcKYb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"-Pbxq2tT3Z4t"},"execution_count":null,"outputs":[]}]}